An important need for any organization is managing and directing/managing the inbound and outbound interactions with customers, and often with real time requirements. Many organizations have multiple channels: customers can visit/search the organization website, make a call to a call center, chat through web chat, visit a branch office, communicate via email, or correspond through the postal service. In the course of these communications a customer also engage economically with the organization by paying for goods/services and/or receiving goods/services from the company. In order to sell more and market goods/services, considerable effort has been expended to direct customer interactions in order to better meet customer needs. Due to the multi-channel nature of customer interactions, many different individuals within the organization will often interact with each customer, and there is a significant need for coordinating, across these different channels, and across time within the same channel.
Moreover, each interaction is complex and it would be useful to define precisely the strategies that are employed by the organization so that experiments and statistical inference may be used to continuously improve interactions with customers. For example, in the area of web chat, certain strategies (including discussing particular stories, introducing well-matched groups of items to purchase, etc. based on customer profiles) can significantly increase sales and improve customer relationships. Organizations are dynamic and are continually releasing new products and services requiring new strategies to be developed and implemented. There may be significant costs in retraining company representatives to use these new strategies and to coordinate across different channels and different interaction sessions.
One increasingly popular approach is to provide employees with an interaction manager (IM) that is available through a computer system and connects through a network to a server which manages the interactions. “Agent work” may include work using a computer with real time network communications and an interactions server with an occasionally-connected augmented visible dashboard. Through connection to one or more servers, an agent communicates with a customer via chat, voice, email, web site, and/or post, for business development purposes. The IM is designed for gathering information associated with customer interactions that occur within interaction sessions, as well as other inbound/outbound communications and behaviors. The IM also provides an augmented computer display dashboard for enriching agent work with offers or recommendations based upon the comprehensive real-time view of customer information, augmented by business rules and/or data mining.
While a typical IM may provide real time offers and/or suggestions for agents using an augmented dashboard, it is not designed to manage the information-state of the customer or to adapt and personalize the interaction to each unique customer. Typical IM systems, for instance, do not perform adaptive natural language generation for agents nor do they provide continuous feedback to agents on the quality of their interaction with the customer. Furthermore, typical IM systems do not adaptively create new concepts to understand customer segments and goals.
Typical IM systems have only very limited knowledge about how customers make decisions Typical IM systems, for instance, do not perform adaptive natural language generation for agents nor do they provide continuous feedback to agents on the quality of their interaction with the customer. Furthermore, typical IM systems do not adaptively create new concepts to understand customer segments and goals.
Typical IM systems cannot achieve these aims because they lack semantically annotated data. Standard systems in the field of natural language understanding require a great deal of semantically annotated data in order to be effective. As customer interaction data is not annotated for customer goals, customer information state, or agent strategy it is difficult for an agent to get adaptive information in real time about the interaction.